The deterministic RNN-based model (Figure REF .(a)) is ConvLSTM [1]}. It follows “sequence to sequence” architecture. It consists of two networks, an encoding network and a decoding network. the initial states and cell outputs of the decoding network are copied from the last state of the encoding network. Both networks are formed by stacking several ConvLSTM layers. The encoder network consists of three stacked stride-1 convLSTM layers with 64, 192 and 192 filters, respectively. The filter size is \(3\times 3\)  for the three layers. We added a downsampling layer before each convLSTM layer. Each downsampling layer contains a stride-2 \(4\times 4\)  convolution followed by Leaky-ReLU non-linearity. The decoder network consists of three stacked stride-1 convLSTM layers with 192, 192 and 64 filters, respectively. The filter size is \(3\times 3\)  for the three layers. We added a upsampling layer after each convLSTM layer. Each downsampling layers contain a stride-2 \(4\times 4\)  deconvolution followed by Leaky-ReLU non-linearity. After the last dwonsampling layer, we added a stride-1 \(3\times 3\)  convolution followed by Leaky-ReLU non-linearity and a stride-1 \(1\times 1\)  convolution \(1\times 1\)  convolutional layer to generate the final prediction.
